Adobe Fireworks CS3 by James Gonzalez

By James Gonzalez

Книга Adobe Fireworks CS3 Adobe Fireworks CS3Книги English литература Автор: Adobe Год издания: 2007 Формат: pdf Страниц: 25 Размер: 10,8 Язык: Английский0 (голосов: zero) Оценка:The excellent resolution for quickly prototyping web content and net software interfacesCreate and edit either vector and bitmap pictures in a single applicationOptimize vector and bitmap photos for the webOffers tight integration with comparable instruments, together with Photoshop CS3, Illustrator CS3, Flash CS3 specialist, Dreamweaver CS3, and Adobe FlexIdeal for internet and interactive content material designers and builders, larger schooling college and scholars, and skilled photograph designers

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It’s hard to get the best of both worlds. As far as I understand, everything’s very manual in the film industry when it comes to things like matting. People will still roto (rotoscope) out stuff by hand if they don’t have a blue screen shot instead of using some of the more advanced computer vision techniques. That’s disappointing since I’d like to see some of the progress that’s made on the academic side come back to the film industry — but getting that reliability is hard. RJR: Can you describe how an artist does blue-screen or green-screen matting in practice?

As in Bayesian matting, initial Gaussian mixture models are fit to the foreground and background intensities inside and outside the box. The GrabCut algorithm iterates three steps until the binary α labels have converged: 1. 2. 3. Each pixel is assigned to one of the foreground (if αi = 1) or background (if αi = 0) Gaussian mixture components. The parameters of each Gaussian mixture component are re-estimated based on pixel memberships. 84) for the pixel-to-terminal weights. The graph cut algorithm is used to update the hard segmentation.

Li et al. 1 to video. 8, but the nodes in the graph (here, image superpixels) are connected both in space and time, with inter-frame edge weights estimated similarly to intra-frame edge weights. Criminisi et al. [107] also posed video segmentation as a conditional random field energy minimized with graph cuts, but added an explicit learned prior on the foreground likelihood at a pixel based on its label in the previous two frames. 21b-c). Wang et al. [530] also proposed a graph-cut-based video segmentation method, but extended the superpixel formation, user stroking, and border matting algorithms to operate natively in the space-time “video volume” formed by stacking the frames at each time instant.

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